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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 4: AAAI-22 Technical Tracks 4

Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding

February 1, 2023

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Authors

Ibrahim Abdelaziz

IBM Research


Julian Dolby

IBM Research


Jamie McCusker

RPI


Kavitha Srinivas

IBM Research


DOI:

10.1609/aaai.v36i4.20363


Abstract:

Code understanding is an increasingly important application of Artificial Intelligence. A fundamental aspect of understanding code is understanding text about code, e.g., documentation and forum discussions. Pre-trained language models (e.g., BERT) are a popular approach for various NLP tasks, and there are now a variety of benchmarks, such as GLUE, to help improve the development of such models for natural language understanding. However, little is known about how well such models work on textual artifacts about code, and we are unaware of any systematic set of downstream tasks for such an evaluation. In this paper, we derive a set of benchmarks (BLANCA - Benchmarks for LANguage models on Coding Artifacts) that assess code understanding based on tasks such as predicting the best answer to a question in a forum post, finding related forum posts, or predicting classes related in a hierarchy from class documentation. We evaluate performance of current state-of-the-art language models on these tasks and show that there is significant improvement on each task from fine tuning. We also show that multi-task training over BLANCA tasks help build better language models for code understanding.

Topics: AAAI

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Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding Proceedings of the AAAI Conference on Artificial Intelligence (2022) 4415-4423.

Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding AAAI 2022, 4415-4423.

Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas (2022). Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding. Proceedings of the AAAI Conference on Artificial Intelligence, 4415-4423.

Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas. Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.4415-4423.

Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas. 2022. Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding. "Proceedings of the AAAI Conference on Artificial Intelligence". 4415-4423.

Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas. (2022) "Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding", Proceedings of the AAAI Conference on Artificial Intelligence, p.4415-4423

Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas, "Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding", AAAI, p.4415-4423, 2022.

Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas. "Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.4415-4423.

Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas. "Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 4415-4423.

Ibrahim Abdelaziz||Julian Dolby||Jamie McCusker||Kavitha Srinivas. Can Machines Read Coding Manuals Yet? – A Benchmark for Building Better Language Models for Code Understanding. AAAI[Internet]. 2022[cited 2023]; 4415-4423.


ISSN: 2374-3468


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